Acosta-Pech Rocío, Crossa José, de Los Campos Gustavo, Teyssèdre Simon, Claustres Bruno, Pérez-Elizalde Sergio, Pérez-Rodríguez Paulino
Colegio de Postgraduados, CP 56230, Montecillos, Edo. De México, México.
Biometrics and Statistics Unit of the International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México DF, México.
Theor Appl Genet. 2017 Jul;130(7):1431-1440. doi: 10.1007/s00122-017-2898-0. Epub 2017 Apr 11.
A new genomic model that incorporates genotype × environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids. The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype × environment interaction (G × E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G × E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G × E in the prediction of untested maize hybrids increases the accuracy of genomic models.
一种纳入基因型×环境互作的新基因组模型提高了对未测试杂交种响应的预测准确性，这些杂交种涉及玉米杂交种的淀粉含量百分比、干物质含量百分比和青贮产量等性状。杂交种性能（HP）的预测在农业育种计划中非常重要。在植物育种中，多环境试验在重要性状的选择中发挥着重要作用，例如跨环境的稳定性、谷物产量和抗虫性。环境条件调节基因表达，导致基因型×环境互作（G×E），因此各个品系在不同环境下性能的估计遗传相关性总结了基因和环境条件的联合作用。本文提出了一种基因组统计模型，该模型纳入了G×E用于一般和特殊配合力，以预测杂交种在不同环境下的性能。所提出的模型也可应用于任何具有不同亲本群体的其他杂交物种。在本研究中，我们使用交叉验证方法评估了两个HP预测模型的预测能力，该方法应用于广泛的玉米杂交数据，包括来自507个马齿型品系和24个硬粒型品系的2724个杂交种，这些杂交种在12年中的58个环境中针对三个性状进行了评估；每年都进行了分析。平均而言，包含一般和特殊配合力与环境互作的基因组模型比不与环境互作的基因组模型具有更强的预测能力（根据性状不同，提高幅度在12%至22%之间）。我们得出结论，在预测未测试的玉米杂交种时纳入G×E可提高基因组模型的准确性。
